What will we learn today?

Scope and Assumptions

• Dis-engangling Causation from Correlation in Effects of Religious Service: Good-Life/ Cooperation
• Focus on “Individual Differences”
• Evolutionary Interests About How These Domains Are Related

Daily Counts of Processed NZAVS Surveys: Repeated Measures –> Causal Insights

Two Questions/ Two Domains

Eligibility

• Responded to the time 10 (2018/19) New Zealand Attitudes Study
• Information about religious service at baseline (yes or no).
• Inverse Probability of Censoring Weights for Attrition
• ‘Censored’ if responded during COVID lockdowns (NZAVS 10)
• A total of 46,377 individuals met these criteria.

Method

• Clearly stated causal estimands
• Semi-parametric Machine Learning
• Censoring models to recover what would have happened to population.
• New Zealand Census Weights to for population inference
• Cross-validation: Models always evaluated on unseen data

Context

Sample Demography

Three Estimand Types

• Binary intervention: everyone at 0 services/month vs. everyone at 1 service/month’
• Increase weekly service attendance unless already attending weekly
• Decrease service attendance to zero unless already not attending

1.1 Three Waves Good Life: ATE Causal Forests and Binary Exposure

Figure 4: ATE: Three-Wave: Binary Intervention

1.2 Good Life: Compare 3 waves Binary vs. 3 waves WEEKLY/ZER0

Figure 5: ATE: Three-wave Shift Intervention

1.3 Sensitivity Analysis: Compare 3 waves Religious Service WEEKLY/NULL with 3 waves Socialising Hours WEEKLY/NULL

Figure 6: ATE: Three-wave Soft Intervention

1.4 Good Life: Compare 3 waves WEEKLY/ZER0 vs 6 waves WEEKLY/ZER0

Figure 7: ATE: Six-wave Soft Intervention GAIN: +1

1.5 Good Life: Compare 6 waves WEEKLY/ZER0 vs 6 waves WEEKLY/NULL

Figure 8: ATE: Six-wave Soft Intervention LOSS: -1

1.6 Good Life: Compare 6 waves WEEKLY/NULL vs 6 waves ZERO/NULL

Figure 9: CATE RATE

SUMMARY STUDY 1: GOOD LIFE

• Depends on which effect (single intervention vs five interventions/e.g. shift up/down)
  
• Signals are detectable in binary intervention but weaker
  
• Unlikely to be due to hours of socialising (an overly conservative sensitivity analysis)
  
• Meaning and Purpose: Strongest Result
• Sexual Satisfaction… (there’s a literature)

2.1 Three Waves Cooperation: ATE Causal Forests and Binary Exposure

Figure 11: ATE: Three-Wave: Binary Intervention

2.2 Three Waves Cooperation: Compare 3 waves Binary vs. 3 waves WEEKLY/ZER0

Figure 12: ATE: Three-wave Shift Intervention

2.3 Three Waves Cooperation: Compare 3 waves WEEKLY/ZER0 vs. 3 waves WEEKLY/NULL

Figure 13: ATE: Three-wave Soft Intervention

2.4 Six Waves Cooperation: Compare 3 waves WEEKLY/ZER0 vs 6 waves WEEKLY/NULL

Figure 14: ATE: Six-wave Soft Intervention WEEKLY/ZER0

2.5 Six Waves Cooperation: Compare 6 waves WEEKLY/NULL vs 6 waves ZERO/NULL

Figure 15: ATE: Six-wave Soft Intervention LOSS: -1

SUMMARY STUDY 2: COOPERATION

• Again Inference depends on which effect we consider (single intervention vs five interventions/e.g. shift up/down)
• Loss of religious service different to gain.
• Not much signal in Social Support or Social Belonging

Policy Trees for The GOOD LIFE

Policy Tree 3.1: Forgiveness Outcome

Figure 17: Policy Trees for Forgiveness Outcome

Policy Tree 2: Gratitude Outcome

Figure 18: Policy Trees for Gratitude Outcome

Policy Tree 3: Hlth Fatigue Outcome

Figure 19: Policy Trees for Hlth Fatigue Outcome

Policy Tree 4: Meaning Sense Outcome

Figure 20: Policy Trees for Meaning Sense Outcome

Policy Tree 5: Neighbourhood Community Outcome

Figure 21: Policy Trees for Neighbourhood Community Outcome

Policy Tree 6: Self Esteem Outcome

Figure 22: Policy Trees for Self Esteem Outcome

Policy Tree for COOPERATION

Policy Tree 1: Donations

Figure 23: Policy Trees for log Charity Donate Outcome

SUMMARY

Supplement 1

S.1 Heterogeneity Treatment Effect Decision Flow

This following flowchart shows the decision logic:

    START: For each model
             |
             v
    STEP 1: EXCLUSION CHECK
    Is RATE QINI < 0 (stat sig) OR RATE AUTOC < 0 (stat sig)?
             |
        +----+----+
        |         |
       YES       NO
        |         |
        v         v
    EXCLUDED    STEP 2: RATE SELECTION CHECK
    (Stop)      Is RATE QINI > 0 (stat sig) OR RATE AUTOC > 0 (stat sig)?
                         |
                    +----+----+
                    |         |
                   YES       NO
                    |         |
                    v         v
                SELECTED   UNCLEAR
                    |         |
                    v         v
                STEP 3: QINI CURVE ANALYSIS
                (Applied to all non-excluded models)
                    |         |
                    v         v
                Already     Check: Is QINI curve positive
                selected     at any spend level?
                            |
                       +----+----+
                       |         |
                      YES       NO
                       |         |
                       v         v
                   SELECTED   UNCLEAR
                             (final)